Theses (Electrical Engineering and Computer Science)http://hdl.handle.net/1957/81632014-12-17T20:00:06Z2014-12-17T20:00:06ZOn edge disjoint Hamiltonian cycles in torus and Gaussian networksAlazemi, Fawaz M.http://hdl.handle.net/1957/546542014-12-17T16:52:48Z2014-12-04T00:00:00ZOn edge disjoint Hamiltonian cycles in torus and Gaussian networks
Alazemi, Fawaz M.
Many algorithms in parallel systems can be easily solved if we can generate a Hamiltonian cycle on the underly network. Finding Hamiltonian cycle is a well known NP-complete problem. For specific instances of regular graphs, such as Torus and Gaussian network, one can easily find Hamiltonian cycles. In this thesis, we present a recurrence function that can generate 2[superscript r] ≥ 1 independent Gray codes from Z[supserscript n][subscript k] where 2[superscript r] ≤ n < 2[superscript r+1]. Such independent Gray codes corresponds to edge disjoint Hamiltonian cycles on the Torus graph T[supserscript n][subscript k] and multidimensional Gaussian network Gα[superscript ⌊n/2⌋], for 1 ≤ 2[superscript r] ≤ n < 2[superscript r+1].
Graduation date: 2015; Access restricted to the OSU Community, at author's request, from Dec. 16, 2014 - Dec. 16, 2015
2014-12-04T00:00:00ZDynamic biasing for ring amplificationFarahbakhshian, Farshadhttp://hdl.handle.net/1957/546352014-12-15T17:16:44Z2014-12-10T00:00:00ZDynamic biasing for ring amplification
Farahbakhshian, Farshad
New amplifier architectures are presented using non-traditional methods of biasing. Time-based dynamic biasing and signal-based dynamic biasing are discussed in the context of new architectures. This includes a new form of ring amplification with a dynamic deadzone, allowing for a structure whose coarse path does not consume static power.
Graduation Date: 2015; Access restricted to the OSU Community, at author's request, from Dec. 12, 2014 - June 12, 2015
2014-12-10T00:00:00ZPersonalizing machine learning systems with explanatory debuggingKulesza, Toddhttp://hdl.handle.net/1957/546222014-12-10T19:14:03Z2014-12-01T00:00:00ZPersonalizing machine learning systems with explanatory debugging
Kulesza, Todd
How can end users efficiently influence the predictions that machine learning systems make on their behalf? Traditional systems rely on users to provide examples of how they want the learning system to behave, but this is not always practical for the user, nor efficient for the learning system. This dissertation explores a different personalization approach: a two-way cycle of explanations, in which the learning system explains the reasons for its predictions to the end user, who can then explain any necessary corrections back to the system. In formative work, we study the feasibility of explaining a machine learning system's reasoning to end users and whether this might help users explain corrections back to the learning system. We then conduct a detailed study of how learning systems should explain their reasoning to end users. We use the results of this formative work to inform Explanatory Debugging, our explanation-centric approach for personalizing machine learning systems, and present an example of how this novel approach can be instantiated in a text classification system. Finally, we evaluate the effectiveness of Explanatory Debugging versus a traditional learning system, finding that explanations of the learning system's reasoning improved study participants' understanding by over 50% (compared with participants who used the traditional system) and participants' corrections to this reasoning were up to twice as efficient as providing examples to the learning system.
Graduation date: 2015
2014-12-01T00:00:00ZComprehensive depletion-mode modeling of oxide thin-film transistorsZhou, Fanhttp://hdl.handle.net/1957/543152014-12-01T17:17:38Z2014-11-21T00:00:00ZComprehensive depletion-mode modeling of oxide thin-film transistors
Zhou, Fan
The primary focus of this thesis is modifying the comprehensive depletion-mode model and extending its applicability to p-channel thin-film transistor (TFT) behavior and subthreshold (subpinchoff) operation. The comprehensive depletion-mode model accurately describes depletion-mode TFT behavior and establishes a set of equations, different from those obtained from square-law theory, which can be used for carrier mobility extraction.
In the modified comprehensive depletion-mode model, interface mobility (mu_interface ) and bulk mobility (mu_bulk ) are distinguished. Simulation results reveal that when square-law theory mobility extraction equations are used to assess depletion-mode TFTs, the estimated interface mobility is often overestimated. In addition, the carrier concentration of a thin channel layer can be estimated from an accurate fitting of measured depletion-mode TFT current-voltage characteristics curves using the comprehensive depletion-mode model.
Graduation date: 2015
2014-11-21T00:00:00Z